crewAI/docs/concepts/processes.mdx

---
title: Processes
description: Detailed guide on workflow management through processes in CrewAI, with updated implementation details.
icon: bars-staggered
---

## Understanding Processes
<Tip>
  Processes orchestrate the execution of tasks by agents, akin to project management in human teams. 
  These processes ensure tasks are distributed and executed efficiently, in alignment with a predefined strategy.
</Tip>

## Process Types

CrewAI supports two process types that determine how tasks are assigned to agents:

- **Static/Assigned Process** (formerly "Sequential"): In this process, each task must be pre-assigned to a specific agent. While the name "Sequential" was previously used, it's important to note that tasks are always executed in the order they are defined, regardless of the process type chosen. The key characteristic of this process is that it requires explicit agent assignments for each task.

- **Dynamic/Unassigned Process** (formerly "Hierarchical"): In this process, you do not have to assign agents to tasks explicitly. Instead, the crew will assess available agents and automatically select the most suitable one for each task based on their roles and expertise. This requires specifying either a manager language model (`manager_llm`) or a custom manager agent (`manager_agent`) to handle the agent selection and task delegation.

- **Consensual Process** (Planned): Aiming for collaborative decision-making among agents on task execution, this process type introduces a democratic approach to task management within CrewAI. It is planned for future development and is not currently implemented in the codebase.

### Process Type Comparison

| Aspect | Static/Assigned Process | Dynamic/Unassigned Process |
|--------|------------------------|---------------------------|
| Agent Assignment | Pre-assigned by developer | Automatic based on agent capabilities |
| Task Order | Sequential (defined order) | Sequential (defined order) |
| Manager Required | No | Yes (manager_llm or manager_agent) |
| Use Case | Fixed workflows with known agent assignments | Dynamic workflows needing flexible assignment |
| Configuration | Simpler setup, explicit control | Requires manager configuration |
| Task-Agent Mapping | One-to-one, defined at creation | Determined during execution |

## The Role of Processes in Teamwork
Processes enable individual agents to operate as a cohesive unit, streamlining their efforts to achieve common objectives with efficiency and coherence.

## Assigning Processes to a Crew
To assign a process to a crew, specify the process type upon crew creation to set the execution strategy. For a hierarchical process, ensure to define `manager_llm` or `manager_agent` for the manager agent.

```python
from crewai import Crew, Agent, Task
from crewai.process import Process
from langchain_openai import ChatOpenAI

# Define agents with specific roles and expertise
researcher = Agent(
    role="Researcher",
    goal="Conduct thorough market analysis",
    backstory="Expert in data analysis and market research"
)

writer = Agent(
    role="Writer",
    goal="Create comprehensive reports",
    backstory="Technical writer with expertise in market analysis"
)

# Example 1: Static/Assigned Process
# Tasks must be explicitly assigned to agents
research_task = Task(
    description="Research emerging market trends",
    agent=researcher  # Explicit agent assignment required
)

writing_task = Task(
    description="Create market analysis report",
    agent=writer,  # Explicit agent assignment required
    context="Use research findings to create a detailed report"
)

# Create crew with Static/Assigned process
static_crew = Crew(
    agents=[researcher, writer],
    tasks=[research_task, writing_task],
    process=Process.sequential  # Note: Using old enum value for backward compatibility
)

# Example 2: Dynamic/Unassigned Process
# Tasks without pre-assigned agents
dynamic_research_task = Task(
    description="Research emerging market trends",
    required_skills=["market analysis", "data interpretation"]  # Help manager select agent
)

dynamic_writing_task = Task(
    description="Create market analysis report",
    context="Use research findings to create a detailed report",
    required_skills=["technical writing", "data visualization"]
)

# Create crew with Dynamic/Unassigned process
dynamic_crew = Crew(
    agents=[researcher, writer],
    tasks=[dynamic_research_task, dynamic_writing_task],
    process=Process.hierarchical,  # Note: Using old enum value for backward compatibility
    manager_llm=ChatOpenAI(model="gpt-4")  # Manager will assign tasks to suitable agents
    # Alternative: Use custom manager agent
    # manager_agent=custom_manager_agent
)
```
**Note:** Ensure `my_agents` and `my_tasks` are defined prior to creating a `Crew` object, and for the hierarchical process, either `manager_llm` or `manager_agent` is also required.

## Static/Assigned Process

This process type requires explicit agent assignments for each task. Tasks are executed in their defined order, with the output of one task serving as context for the next. This approach provides direct control over which agent handles each specific task.

To customize task context, utilize the `context` parameter in the `Task` class to specify outputs that should be used as context for subsequent tasks.

## Dynamic/Unassigned Process

This process type enables automatic agent selection through a manager component. You must specify either a custom manager agent or a manager language model (`manager_llm`). The manager oversees task execution by:
- Analyzing task requirements
- Selecting the most suitable agent based on roles and expertise
- Delegating tasks automatically
- Reviewing outputs and assessing task completion

## Choosing the Right Process

When deciding between Static/Assigned and Dynamic/Unassigned processes, consider these technical factors:

### Static/Assigned Process
Consider this process when:
- Your workflow has predefined task-agent mappings
- You need deterministic agent assignments for auditing or compliance
- You want to minimize runtime overhead (no manager required)
- You have specific agents optimized for particular tasks
- You need fine-grained control over task execution

### Dynamic/Unassigned Process
Consider this process when:
- Your agent pool has overlapping capabilities
- Task requirements are determined at runtime
- You need failover capabilities between agents
- You have a manager (LLM or agent) to handle assignment logic
- You want to scale agent pools without modifying task definitions

### Technical Considerations
- **Performance**: Static/Assigned processes have lower overhead as they skip the agent selection phase
- **Scalability**: Dynamic/Unassigned processes better handle changes in agent availability
- **Maintenance**: Static assignments require updating task definitions when agent roles change
- **Error Handling**: Dynamic processes can potentially reassign tasks on agent failures

## Technical Implementation Details

### Process Class
The `Process` class is implemented as an enumeration (`Enum`), ensuring type safety and restricting process values to the defined types:
```python
class Process(Enum):
    sequential = "sequential"     # Static/Assigned process
    hierarchical = "hierarchical" # Dynamic/Unassigned process
```

### Default Configuration
- The default process type is `Process.sequential` (Static/Assigned)
- When using `Process.hierarchical`, a manager (either `manager_llm` or `manager_agent`) must be provided

### Version Compatibility
- Since v1.0.0: Original process types (`sequential`, `hierarchical`)
- Current version maintains the same enum values for backward compatibility
- Future versions will continue supporting these values while using new terminology in documentation

### Error Handling
Common error scenarios and their solutions:
```python
# Error: Missing manager in Dynamic/Unassigned process
crew = Crew(
    agents=[agent1, agent2],
    tasks=[task1, task2],
    process=Process.hierarchical
    # Error: ValueError: Manager (manager_llm or manager_agent) is required for hierarchical process
)

# Error: Missing agent assignment in Static/Assigned process
task = Task(description="Task without agent")  # Missing agent assignment
crew = Crew(
    agents=[agent1, agent2],
    tasks=[task],
    process=Process.sequential
    # Error: ValueError: Agent assignment required for all tasks in sequential process
)
```

## Conclusion

The structured collaboration facilitated by processes within CrewAI is crucial for enabling systematic teamwork among agents. 
This documentation has been updated to reflect the latest features, enhancements, and the planned integration of the Consensual Process, ensuring users have access to the most current and comprehensive information.